Abstract
Exploratory data analysis involves making a series of complex decisions: what should I explore? what questions should I ask? As users do not have good knowledge about the data they are exploring, making these decisions is non-trivial. In making these decisions, heuristics are often applied, potentially causing a biased exploration path. While breadth-oriented data exploration presents a promising solution to rectifying a biased exploration path, how to design such systems is yet to be explored. In this Chapter, we propose three considerations in designing systems that support breadth-oriented data exploration. To demonstrate the utility of these design considerations, we describe a hypothetical breadth-oriented system. We argue that these design considerations pave the way for understanding how breadth-oriented exploration mitigates biases in exploratory data analysis.
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Law, PM., Basole, R.C. (2018). Designing Breadth-Oriented Data Exploration for Mitigating Cognitive Biases. In: Ellis, G. (eds) Cognitive Biases in Visualizations. Springer, Cham. https://doi.org/10.1007/978-3-319-95831-6_11
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DOI: https://doi.org/10.1007/978-3-319-95831-6_11
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